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	This ontology document is licensed under the Creative Commons
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 <channel rdf:about="http://ebiquity.umbc.edu//tags/html/?t=latent+dirichlet+allocation">
  <cc:license rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  <title><![CDATA[UMBC ebiquity RSS Tag Search]]></title>
  <link><![CDATA[http://ebiquity.umbc.edu//tags/html/?t=latent+dirichlet+allocation]]></link>
  <description><![CDATA[UMBC ebiquity RSS Tag Search for latent dirichlet allocation]]></description>
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      <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/909/Understanding-the-Logical-and-Semantic-Structure-of-Large-Documents"/>
      <rdf:li resource="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs"/>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/478/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of
text documents and have become an important technique for systems that
measure document similarity for classification, clustering,
segmentation, entity linking and more.  While they have been applied
to some non-text domains, their use for semi-structured graph data,
such as RDF, has been less explored.  We present a framework for
applying topic modeling to RDF graph data and describe how it can be
used in a number o...]]></description>
  <dc:date>2015-09-21</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection">
  <title><![CDATA[Evaluating Causal AI Techniques for Health  Misinformation Detection]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/1187/Evaluating-Causal-AI-Techniques-for-Health-Misinformation-Detection</link>
  <description><![CDATA[Abstract—The proliferation of health misinformation on social media, particularly regarding chronic conditions such as diabetes, hypertension, and obesity, poses significant public health risks. This study evaluates the feasibility of leveraging Natural Language Processing (NLP) techniques for real-time misinformation detection and classification, focusing on Reddit discussions. Using logistic regression as a baseline model, supplemented by Latent Dirichlet Allocation (LDA) for topic modeli...]]></description>
  <dc:date>2025-03-17</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration">
  <title><![CDATA[Two Tier Analysis of Social Media Collaboration for Student Migration]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/877/Two-Tier-Analysis-of-Social-Media-Collaboration-for-Student-Migration</link>
  <description><![CDATA[Global adoption of Social Media as the preferred medium for collaboration and information exchange is increasingly reshaping social realities and facilitating new research methodologies in various disciplines. Social Media applications are collecting a large amount of User-Generated Content (UGC) and web data that contains knowledge about novel approaches of global collaboration between people. We have done a detailed study of the factors that lead to student migration, as espoused by social ...]]></description>
  <dc:date>2019-12-14</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/909/Understanding-the-Logical-and-Semantic-Structure-of-Large-Documents">
  <title><![CDATA[Understanding the Logical and Semantic Structure of Large Documents]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/909/Understanding-the-Logical-and-Semantic-Structure-of-Large-Documents</link>
  <description><![CDATA[Current language understanding approaches are mostly focused on small documents, such as newswire articles, blog posts, and product reviews. Understanding and extracting information from large documents like legal documents, reports, proposals, technical manuals, and research articles is still a challenging task. Because the documents may be multi-themed, complex, and cover diverse topics. The content can be split into multiple files or aggregated into one large file. As a result, the content...]]></description>
  <dc:date>2018-05-01</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs">
  <title><![CDATA[Topic Modeling for RDF Graphs]]></title>
  <link>http://ebiquity.umbc.edu/paper/html/id/714/Topic-Modeling-for-RDF-Graphs</link>
  <description><![CDATA[Topic models are widely used to thematically describe a collection of text documents and have become an important technique for systems that measure document similarity for classification, clustering, segmentation, entity linking, and more.  While they have been applied to some non-text domains, their use for semi-structured graph data, such as RDF, has been less explored.  We present a framework for applying topic modeling to RDF graph data and describe how it can be used in a number of link...]]></description>
  <dc:date>2015-10-12</dc:date>
 </item>
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